PhD Studentship Vacancies
Applications for full-time funded PhD studentships based at The Open University in Milton Keynes starting on 1st October 2023 are now closed. Studentships starting on 1st October 2024 will be advertised in January 2024. If you are interested in applying for a PhD with us, please have a look at the guidelines below.
Applicants are required to develop a project proposal as part of the application process and should contact the named supervisor(s) for their topic of interest to get more information and guidance on developing their application. Visit our FAQs for more helpful information about our PhD studentships. Further details on the application process are available on the 'How to Apply' and 'Writing a PhD Proposal' pages.
The following list of topics, grouped by research area, are available this year.
Area 1. Blockchains and Decentralised Systems
A blockchain is an open, decentralised and trustable system. Without the need for any central control or mediator, blockchains allow us to rethink applications in a decentralised way, providing a provenance protocol for sharing data across disparate semi-trusting organisations.
Decentralised Online Media Commenting for 21st Century Digital Journalism
Media and News companies are increasingly shutting down online commenting sections. This mostly stems from the lack of existing discussion platforms that can effectively respond to the requirements of civil, accountable discussion, including respect for anonymity and authenticity of people, facts and opinions. This PhD aims to study, design and develop a novel Digital Journalism discussion tool, which goes beyond these limitations and offers a viable alternative for media companies to re-open commenting to its readers. This technology will build on Artificial Intelligence and Decentralised Systems approaches to facilitate healthy discussions, promoting civilised free speech in online media.
Supervisors: Prof Anna De Liddo
Keywords: Online Media Commenting Digital Journalism Social Justice Anonymous Reputation Decentralised Systems Artificial Intelligence
Skillset: Programming (Web development, Distributed Ledgers) Statistics User Studies/Research A passion for Digital Journalism
Decentralised, Trusted Explainable AI Reasoning
Over recent times concerns have been raised on how companies sometimes abuse personal user data within their internal AI reasoning systems. What if we could decentralise the reasoning and put it directly in the hands of users? Can we create a framework where automated reasoning can be carried out without the need for a central authority? How can we facilitate trust in automated reasoning processes and explain this clearly to non-computer scientists? This research is important for the development of more transparent, trustworthy AI systems that can be trusted to make sometimes life-changing decisions in complex, dynamic environments.
Supervisors: Prof John Domingue and Dr Aisling Third
Keywords: Knowledge Graphs Deep Learning Trust Blockchains Decentralised Ledgers
Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data Knowledge Graphs Deep Learning
Facilitating Trust in Collaborative AI and Human Ecosystems with Distributed Ledgers
This PhD will focus on combining AI and blockchain technology to improve trust and cooperation between AI systems and humans in collaborative settings. The research aims to explore how distributed ledger technology can be used to enhance communication and coordination between AIs and humans, with the goal of creating trusted, efficient and effective collaborative ecosystems. The findings of this research could have important implications for a wide range of fields, including finance, supply chain management, and security. Ultimately, the goal is to provide a foundation to help AIs and humans work together more effectively, leading to better outcomes for all parties involved.
Supervisors: Prof John Domingue, Dr Amel Bennaceur and Dr Aisling Third
Keywords: Artificial Intelligence Autonomous Agents Trust Blockchains Decentralised Ledgers Linked Data Knowledge Graph
Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data Autonomous Agents
This PhD will focus on the technical challenges and potential applications of decentralised personalised individually-owned AI services running on personal devices. The candidate will explore questions, such as a) what the AI agents could learn with limited computational capabilities and user-specific data from individuals and households and b) how these agents would act as users' proxies and interact with available services and resources such as open knowledge bases and other centralised/specialised AI services. The candidate will develop new approaches to encoding long-term goals for conversational agents co-developed with their owner-users in real-life scenarios of lifelong wellbeing applications.
Supervisors: Dr Alessio Antonini and Dr Iman Naja
Keywords: Personal Computing Positive Computing Distributed Multi-Agent Systems Swarm Agents Data Ownership
Skillset: Programming (mobile development, web development, graph databases) Multi-Agent Systems Conversational Agents (e.g., chatbots)
Self-sovereign Valuable Personal Data
Recent events with the acquisition of Twitter have encouraged people to explore decentralised and federated services such as Mastodon as an alternative, and technologies like self-sovereign identity and Solid personal data pods take the idea of a decentralised Web seriously. These architectures challenge current assumptions about trust in data, its contents, and how it is used. This PhD will investigate how personal data and trust are related in the context of decentralised data and identity technologies. Possible application areas include education, healthcare, social media, and equitable access to data infrastructure and value for marginalised groups. This PhD will build on existing work where we have created a framework (LinkChains) for handling personal and sensitive data which combines decentralised platforms with cryptographic and distributed ledger verification.
Supervisors: Dr Aisling Third and Prof John Domingue
Keywords: Identity Self-sovereignty Blockchain Decentralised Ledgers Linked Data Knowledge Graph Solid EDI
Skillset: Software and web development Blockchain (e.g., Ethereum) Linked Data
Area 2. Computational Social Science
Computational Social Science research targets the enhancement of critical societal issues through the use of Artificial Intelligence solutions. This research aligns with the core values of The Open University, and has contributed to urgent and vital topics, such as misinformation detection, online radicalisation and extremism, crisis management, and climate change.
AI Fairness for all Living Beings
The last few years have seen an important change in the mindset of AI research and development, with multiple communities raising their voices about the negative impact of algorithmic biases and discrimination, as well as the risks of using AI for decision making without appropriate assessment and regulation. This PhD aims to take a step further and study how AI technology may affect not only humans, but all living beings, extending the notion of fairness in the development of AI.
Supervisors: Prof Miriam Fernandez and Prof Clara Mancini
Keywords: Artificial Intelligence Machine Learning Fairness Animal-Computer Interaction
Skillset: Computer programming Machine Learning Social Science Research (desired) Animal-Computer Interaction
Assessing and Mitigating the Impact of Global Phenomena, Geopolitical Factors, and Bias on Research
The scientific enterprise is affected by global phenomena, such as the COVID-19 pandemic, geopolitical factors and different kinds of bias. This PhD project aims at shedding light on these issues and assessing their impact across gender, countries, disciplines and others. The main objective is to exploit large-scale datasets of scholarly knowledge, i.e., scientific knowledge graphs, to analyse collaboration, productivity, and other factors with the aim of understanding the extent of the phenomenon and identifying strategies to make scientific research more inclusive and resilient to external factors.
Supervisors: Dr Francesco Osborne, Dr Angelo Salatino and Prof Enrico Motta
Keywords: Scientometrics Scholarly Analytics Covid-19 Scholarly Data Semantic Web Science of Science Social Science Knowledge Graphs
Skillset: Interest/expertise in Science Interest/expertise in Ethics and Justice Data Science Computer programming Network Science Data Integration
Intersectional Hateful Speech
Hateful speech online is a threat to civil society, and the advancement of equality, diversity and inclusion. Many approaches to investigating hateful speech online, particularly when supported by computational approaches for automated detection, focus on either generally offensive and toxic language, or hate toward specific groups. Intersectional forms of hate that relate to more than one protected characteristic are more difficult to spot and tackle. In addition, the focus for computational approaches has been on detection and tracking, rather than understanding or mitigating the impacts of online harm. In this PhD, you are welcome to propose any topic that addresses intersectional hate on the web, and that incorporates computational approaches for understanding the problem or mitigating it.
Supervisors: Prof Miriam Fernandez
Keywords: Intersectionality Hate Speech Machine Learning Online Harm
Skillset: Computer programming Machine Learning (desired) Social Network Analysis Qualitative Analysis Artificial Intelligence for Equality Diversity and Inclusion (AI4EDI)
Misinformation, Prejudice and Fact-Checking
Studies have shown that prejudice is one of the biggest factor behind the spread of false information on social media. Although much research has investigated who spreads misinformation online, less research has investigated the victims of misinformation and the role of fact-checking in tackling such issue. The purpose of this PhD is to better understand the role and effect of fact-checking in tackling various forms of prejudices (e.g., gender identity, sexism, social status, etc.) in relation to misinformation on social media. As part of this PhD, you will analyse the co-spreading patterns of misinformation and fact-checks on social media and how successful are fact-checks in reducing specific forms of prejudice.
Supervisors: Dr Gregoire Burel and Prof Harith Alani
Keywords: Fact-Checking Misinformation Co-Spread Analysis Policy Social Media
Skillset: Computer programming NLP Machine Learning Social Network Analysis Large-scale data analysis
Misinformation correction is a complex process, both socially and technically, with no one-size-fits-all solution. The purpose of this PhD is to study the use of social media bots to deliver, and evaluate, different misinformation correction approaches for different users and under different conditions (e.g. timing, topic, platform). As a result of the strong research track record in this lab, this work is expected to pioneer the field of personalised computational misinformation correction.
Supervisors: Prof Harith Alani, Dr Gregoire Burel, Dr Lara Piccolo
Keywords: Misinformation Social Media Data Science
Skillset: Computer programming Machine Learning Natural Language Processing Human-Computer Interaction
Human AI Learning Through Dialogic Interfaces
This PhD research will explore novel collaborative learning environments proposed and appraised using Bakhtin's dialogical philosophy underlying the use of language (text and visual) and interactions to facilitate collaborative learning (human and machine learning) and meaning making (Bakhtin, 1984; Trausan-Matu et al., 2021). The emphasis is on the design and evaluation of Human-AI collaborations which support group cognition for example by supporting meaning negotiation, knowledge building and dialogical interactions. Projects are welcomed for integrating 'dialogic AI participation' in small group collaborations leading to decision making, problem-solving and consensus building such as in wikis, social media technologies and citizen science platforms.
Supervisors: Dr Nirwan Sharma, Prof Advaith Siddharthan and Prof Stefan Rueger
Keywords: Collaborative Learning Human-Computer Interaction Dialogism Machine Learning Artificial Intelligence Citizen Science
Skillset: Programming Machine Learning Mixed methods User evaluations
Hybrid Intelligence for Knowledge Graph Construction
The project aims at designing novel methods for constructing Knowledge Graphs (KG) integrating data from heterogeneous sources, combining symbolic (rules, plans) and subsymbolic AI (machine/deep learning). The candidate will have a strong interest in RDF, SPARQL, and similar formalisms (OWL, SHACL) and will contribute to addressing issues in KG construction and application such as (1) automating KG generation from structured or unstructured data sources; (2) accessing non-RDF resources with SPARQL (Virtual Knowledge Graphs); (3) improving the usability of KG construction systems for non-expert users.
Supervisors: Dr Enrico Daga and Dr Paul Mulholland
Keywords: Data Integration Semantic Web
Skillset: (required) Programming, Basics of Artificial Intelligence Strong interest in Semantic Web technologies
Open Research Graph
This project will develop novel AI methods for knowledge graph generation from large quantities of research texts. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at core.ac.uk, which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case in cooperation with several companies.
Supervisors: Dr Petr Knoth and Dr David Pride
Keywords: Knowledge Graph Research Graph Machine Learning Artificial Intelligence Big Data Open Science Open Access
Skillset: Natural Language Processing Machine Learning Information Retrieval Data Mining Big Data
Responsible Use of AI in Recommender Systems for Finding Experts
The objective of this project is to develop innovative AI methods for identifying experts possessing highly specific knowledge and skills. The process of finding experts with relevant skills is currently too resource intensive and prone to biases. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at core.ac.uk and which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case with scientific publishers / funders.
Supervisors: Dr David Pride and Dr Petr Knoth
Keywords: Machine Learning Artificial Intelligence Big Data Open Science Open Access
Skillset: Natural Language Processing Machine Learning Information Retrieval Data Mining Big Data
Understanding and Improving the Relationship Between Academia, Industry, and Society
The main objective of this project is to analyse the relation between academia, industry, and society with the aim of understanding how these stakeholders influence each other and producing novel solutions to improve their knowledge flow and accelerate scientific progress. In particular, we will focus on analysing how new ideas and technologies from academia are implemented in concrete products by industry and adopted by society. The student will integrate and leverage information from heterogeneous sources (e.g., scientific articles, patents, social media, news) and design AI-based analytical solutions to investigate the dynamics in this space and improve knowledge exchange.
Supervisors: Dr Angelo Salatino, Dr Francesco Osborne and Prof Enrico Motta
Keywords: Scientometrics Scholarly Analytics Scholarly Data Semantic Web Science of Science Social Science Knowledge Graphs
Skillset: Data Science Programming Network Science Data Mining Data Integration Visual Analytics
Area 3. Data Science and Extended Intelligence
Data Science and Extended Intelligence go beyond efficient data infrastructure and engineering, it studies data empowered human processes that lead to smarter, fairer, more sustainable and equitable ways of living.
Artificial Intelligence for Large-Scale Analysis of the News Dynamics
This project aims to design novel AI techniques for modelling the news agenda at scale, focusing in particular on understanding the dynamics of topics (what subjects are covered in the news) and viewpoints (what perspectives are covered for a given topic) over time. To this purpose, the candidate will develop novel approaches that will combine machine learning and natural language processing techniques for identifying topics and viewpoints at scale and across different sources. Key research challenges here include developing i) methods that can identify more granular and user-friendly characterizations of topics in comparison with current taxonomies and unsupervised machine learning techniques and ii) methods that can effectively identify and distinguish alternative viewpoints, without resorting to pre-defined coarse-grained classifications (e.g., liberal vs conservative viewpoints).
Supervisors: Prof Enrico Motta, Dr Enrico Daga and Dr Francesco Osborne
Keywords: Data Science Deep Learning Knowledge Graphs Natural Language Processing Information Extraction
Skillset: Programming Information Extraction Knowledge Graphs Interest/expertise in news and media
Many museums have a participation gap: the collections and exhibitions predominantly attract physical and virtual visitors that are white and from higher socioeconomic groups. The problem is not cost, rather many people perceive a lack of relevance in what is offered by cultural institutions. Citizen Curation has been proposed as a solution, in which the public are supported in developing and sharing their own interpretations and responses to cultural works, widening the range of voices presented in the museum. This PhD will draw on work in HCI and Digital Humanities to develop ways in which the public can be supported in developing and sharing their responses to artworks, and the museum can be supported in managing those contributions and using them as part of their public offering.
Supervisors: Dr Paul Mulholland and Dr Enrico Daga
Keywords: Citizen Curation Museums Participation Gap
Skillset: HCI Web development Digital Humanities Interest in cultural engagement
Emerging Technologies in Higher Education
The use of technology to enhance the learning experience is a vibrant research area transforming higher education. This PhD will investigate the potential of new and emerging technologies in education, for example exploring AI solutions for facilitating personalised learning and self-regulated learning, supporting lifelong learners in their personal and professional progression, exploring the use of new forms of accreditation such as micro-credentials, etc. This PhD will build upon the results of recent European-funded research projects, including QualiChain and the OpenLang Network.
Supervisors: Dr Alexander Mikroyannidis and Dr Trevor Collins
Keywords: Technology-Enhanced Learning Educational Technology Artificial Intelligence Personalised Learning Self-Regulated Learning Lifelong Learning Micro-Credentials
Skillset: Education skills/interest Technology skills/interest Evaluation studies Research Methods Written and oral communication
The Role of Technology in Fieldwork Education
Within the geosciences, biosciences and environmental sciences fieldwork is seen as a signature pedagogy that provides opportunities for students to put their learning into use, develops students' collaboration and team working skills, and fosters students' sense of belonging within their discipline. This PhD will investigate the role technology plays in fieldwork education. Depending on the applicants' interests and experience, the project may evaluate existing practice or develop and trial new applications. Potential areas of interest include: the use of technology to enhance in-field teaching; the affordances and limitations of virtual field experiences; and/or the use of web broadcasting and streaming technologies to provide remote access to field sites and field scientists.
Supervisors: Dr Trevor Collins, Dr Sarah Davies and Dr Karen Kear
Keywords: Educational Technology Experiential Learning Fieldwork Education Technology-Enhanced Learning
Skillset: Evaluation studies Research Methods Technology development or application Written and oral communication
Area 4. New Media in Society
New Media and Society research aims at going beyond the study of Computing and ICT from a technology perspective, and looks at improving our understating human values and the impact of technology innovations on people's lives and their communities. This research particularly looks at ways to use new media to promote social justice and tackle complex societal challenges of inclusion and disadvantage.